Search Results for author:
Found 14 papers, 2 papers with code
Developing Gridded Emission Inventory from High-Resolution Satellite Object Detection for Improved Air Quality Forecasts
This study presents an innovative approach to creating a dynamic, AI based emission inventory system for use with the Weather Research and Forecasting model coupled with Chemistry (WRF Chem), designed to simulate vehicular and other anthropogenic emissions at satellite detectable resolution.
CloudSense: A Model for Cloud Type Identification using Machine Learning from Radar data
The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation.
Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots
ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data.
Mind meets machine: Unravelling GPT-4's cognitive psychology
Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning.
DeepClouds.ai: Deep learning enabled computationally cheap direct numerical simulations
Simulation of turbulent flows, especially at the edges of clouds in the atmosphere, is an inherently challenging task.
Trustworthy modelling of atmospheric formaldehyde powered by deep learning
Dynamic atmospheric chemistry models struggle to simulate atmospheric formaldehyde and often overestimate by up to two times relative to satellite observations and reanalysis.
Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA
We show the development of a high-resolution gridded precipitation product (300 m) from a coarse (10 km) satellite-based product (JAXA GsMAP).
On the modern deep learning approaches for precipitation downscaling
To test the efficacy of different DL approaches, we apply four different methods of downscaling and evaluate their performance.
Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction
We augment the output of the well-known NWP model CFSv2 with deep learning to create a hybrid model that improves short-range global precipitation at 1-, 2-, and 3-day lead times.
GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications
We introduce University of Texas - Global Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 cities or locales worldwide.
Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia
This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied.
Quantum Artificial Intelligence for the Science of Climate Change
Climate change has become one of the biggest global problems increasingly compromising the Earth's habitability.
Deep learning for improved global precipitation in numerical weather prediction systems
This study is a proof-of-concept showing that residual learning-based UNET can unravel physical relationships to target precipitation, and those physical constraints can be used in the dynamical operational models towards improved precipitation forecasts.
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region
In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season.
